Define adoption and behavioral success frameworks, including leading and lagging indicators and measurement cadence.
Analyze usage patterns, engagement signals, and behavioral trends to identify drivers of adoption and areas of friction.
Integrate qualitative feedback (surveys, interviews) with quantitative metrics to provide actionable insights.
Design and build adoption dashboards using Power BI, Tableau, or similar BI tools.
Partner with data and engineering teams to model adoption datasets in Snowflake or comparable data platforms.
Create mock‑up dashboards (e.g., Figma) to align stakeholders on metric intent and visualization approach.
Document data sources, joins, calculations, and metric definitions to ensure reporting consistency and trust.
Establish governance for adoption reporting, including metric definitions, refresh cadence, ownership, and data quality checks.
Ensure accurate, timely, and auditable visibility of adoption metrics for internal teams and dealer audiences.
Create repeatable documentation and standards to support scalable reporting.
Partner closely with Product, DACS, Dealers, Customer Support, and Industry Partners to align on adoption insights and priorities.
Research industry trends and best practices in digital and AI adoption measurement and incorporate learnings into internal approaches.
Communicate findings clearly to technical and non‑technical audiences.

Extensive experience analyzing user behavior, engagement, or digital adoption data.
Ability to define KPIs and translate behavioral signals into business and product insights.
Hands‑on experience with Power BI, Tableau, or similar visualization tools.
Experience working with Snowflake or similar cloud data platforms.
Strong understanding of metric design, data validation, and reporting best practices.
Ability to translate complex data into clear, actionable insights.
Experience producing data dictionaries, metric definitions, dashboards, and analytical documentation.
Comfortable operating as a first‑of‑role individual contributor.
Strong cross‑functional collaboration skills with product, analytics, and customer‑facing teams.
Familiarity with AI‑enabled digital products is a plus.
Typically, 7+ years or more of professional experience.
Experience around software engineering, analytics, artificial Intelligence.
Extensive experience with managing multiple projects and workstreams.
Proven ability to develop innovative products, streamline complex processes, and manage strategic roadmaps to drive project success.

Lead Business Intelligence Analyst

Key skills